scholarly journals A Survey of Machine Learning Techniques for Video Quality Prediction from Quality of Delivery Metrics

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2851
Author(s):  
Obinna Izima ◽  
Ruairí Fréin ◽  
Ali Malik
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Video Streaming ◽  
Video Quality ◽  
Quality Prediction ◽  
Learning Techniques ◽  
Quality Of Delivery ◽  
Streaming Services ◽  
End To End

A growing number of video streaming networks are incorporating machine learning (ML) applications. The growth of video streaming services places enormous pressure on network and video content providers who need to proactively maintain high levels of video quality. ML has been applied to predict the quality of video streams. Quality of delivery (QoD) measurements, which capture the end-to-end performances of network services, have been leveraged in video quality prediction. The drive for end-to-end encryption, for privacy and digital rights management, has brought about a lack of visibility for operators who desire insights from video quality metrics. In response, numerous solutions have been proposed to tackle the challenge of video quality prediction from QoD-derived metrics. This survey provides a review of studies that focus on ML techniques for predicting the QoD metrics in video streaming services. In the context of video quality measurements, we focus on QoD metrics, which are not tied to a particular type of video streaming service. Unlike previous reviews in the area, this contribution considers papers published between 2016 and 2021. Approaches for predicting QoD for video are grouped under the following headings: (1) video quality prediction under QoD impairments, (2) prediction of video quality from encrypted video streaming traffic, (3) predicting the video quality in HAS applications, (4) predicting the video quality in SDN applications, (5) predicting the video quality in wireless settings, and (6) predicting the video quality in WebRTC applications. Throughout the survey, some research challenges and directions in this area are discussed, including (1) machine learning over deep learning; (2) adaptive deep learning for improved video delivery; (3) computational cost and interpretability; (4) self-healing networks and failure recovery. The survey findings reveal that traditional ML algorithms are the most widely adopted models for solving video quality prediction problems. This family of algorithms has a lot of potential because they are well understood, easy to deploy, and have lower computational requirements than deep learning techniques.

Machine Learning-Based Subjective Quality Estimation for Video Streaming Over Wireless Networks

2019 ◽  
pp. 235-254
Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Video Streaming ◽  
Video Transmission ◽  
Video Quality ◽  
Subjective Quality ◽  
Perceptual Quality ◽  
Support Vector ◽  
Machine Learning Model

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.

scholarly journals Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2297
Author(s):  
Kyeongseon Kim ◽  
Dohyun Kwon ◽  
Joongheon Kim ◽  
Aziz Mohaisen
Keyword(s):  
Video Streaming ◽  
Waiting Time ◽  
Quality Of Experience ◽  
Video Quality ◽  
Live Video ◽  
Streaming Services ◽  
Online Streaming ◽  
Over The Top ◽  
User Content

As the demand for over-the-top and online streaming services exponentially increases, many techniques for Quality of Experience (QoE) provisioning have been studied. Users can take actions (e.g., skipping) while streaming a video. Therefore, we should consider the viewing pattern of users rather than the network condition or video quality. In this context, we propose a proactive content-loading algorithm for improving per-user personalized preferences using multinomial softmax classification. Based on experimental results, the proposed algorithm has a personalized per-user content waiting time that is significantly lower than that of competing algorithms.

scholarly journals A quality of experience-aware cross-layer architecture for optimizing video streaming services

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Video Quality ◽  
The Internet ◽  
Cross Layer ◽  
Video Traffic ◽  
Adaptive Architecture ◽  
Streaming Services ◽  
Simulation Results

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

Network analysis on Skype end-to-end video quality

2015 ◽  
Vol 11 (1) ◽  
pp. 17-42 ◽  
Author(s):  
George Exarchakos ◽  
Luca Druda ◽  
Vlado Menkovski ◽  
Antonio Liotta
Keyword(s):  
Real Time ◽  
Video Streaming ◽  
Video Quality ◽  
Adaptive Streaming ◽  
Content Type ◽  
Adaptive Mechanisms ◽  
Qos Parameters ◽  
End To End ◽  
Motion Video

Purpose – This paper aims to argue on the efficiency of Quality of Service (QoS)-based adaptive streaming with regards to perceived quality Quality of Experience (QoE). Although QoS parameters are extensively used even by high-end adaptive streaming algorithms, achieved QoE fails to justify their use in real-time streaming videos with high motion. While subjective measurements of video quality are difficult to be applied at runtime, objective QoE assessment can be easier to automate. For end-to-end QoS optimization of live streaming of high-motion video, objective QoE is a more applicable approach. This paper contributes to the understanding of how specific QoS parameters affect objective QoE measurements on real-time high-motion video streaming. Design/methodology/approach – The paper approached the question through real-life and extensive experimentation using the Skype adaptive mechanisms. Two Skype terminals were connected through a QoS impairment box. A reference video was used as input to one Skype terminal and streamed on one direction. The impairment box was stressing the stream with different conditions. Received video was stored and compared against the reference video. Findings – After the experimental analysis, the paper concludes that adaptive mechanisms based on QoS-related heuristics fail to follow unexpected changes to stream requirements. High-motion videos are an example of this variability, which makes the perceived quality sensitive to jitter more than to packet loss. More specifically, Skype seems to use if-else heuristics to decide its behavior to QoS changes. The weaknesses to high-motion videos seem to lie on this rigidity. Research limitations/implications – Due to the testbed developed, the results may be different if experiments are run over networks with simultaneous streams and a variety of other traffic patterns. Finally, other streaming clients and algorithms would contribute to a more reliable generalization. Practical implications – The paper motivates video streaming engineers to emphasize their efforts toward QoE and end-to-end optimization. Originality/value – The paper identifies the need of a generic adaptive streaming algorithm able to accommodate a big range of video characteristics. The effect of QoS variability to high-motion video streaming helps in modeling and design.

scholarly journals Buffer evaluation model and scheduling strategy for video streaming services in 5G-powered drone using machine learning

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yu Su ◽  
Shuijie Wang ◽  
Qianqian Cheng ◽  
Yuhe Qiu
Keyword(s):  
Machine Learning ◽  
Video Streaming ◽  
Video Transmission ◽  
Evaluation Model ◽  
Video Stream ◽  
Traffic Load ◽  
Model Based ◽  
Streaming Services ◽  
Reward Mechanism

AbstractWith regard to video streaming services under wireless networks, how to improve the quality of experience (QoE) has always been a challenging task. Especially after the arrival of the 5G era, more attention has been paid to analyze the experience quality of video streaming in more complex network scenarios (such as 5G-powered drone video transmission). Insufficient buffer in the video stream transmission process will cause the playback to freeze [1]. In order to cope with this defect, this paper proposes a buffer starvation evaluation model based on deep learning and a video stream scheduling model based on reinforcement learning. This approach uses the method of machine learning to extract the correlation between the buffer starvation probability distribution and the traffic load, thereby obtaining the explicit evaluation results of buffer starvation events and a series of resource allocation strategies that optimize long-term QoE. In order to deal with the noise problem caused by the random environment, the model introduces an internal reward mechanism in the scheduling process, so that the agent can fully explore the environment. Experiments have proved that our framework can effectively evaluate and improve the video service quality of 5G-powered UAV.

Quality of Experience Models for Multimedia Streaming

2012 ◽  
pp. 112-130 ◽  
Author(s):  
Vlado Menkovski ◽  
Georgios Exarchakos ◽  
Antonio Liotta ◽  
Antonio Cuadra Sánchez
Keyword(s):  
Machine Learning ◽  
Quality Of Experience ◽  
Prediction Models ◽  
Multimedia Streaming ◽  
Accurate Estimation ◽  
Management Service ◽  
Efficient Management ◽  
Learning Techniques ◽  
Streaming Services

Understanding how quality is perceived by viewers of multimedia streaming services is essential for efficient management of those services. Quality of Experience (QoE) is a subjective metric that quantifies the perceived quality, which is crucial in the process of optimizing tradeoff between quality and resources. However, accurate estimation of QoE often entails cumbersome studies that are long and expensive to execute. In this regard, the authors present a QoE estimation methodology for developing Machine Learning prediction models based on initial restricted-size subjective tests. Experimental results on subjective data from streaming multimedia tests show that the Machine Learning models outperform other statistical methods achieving accuracy greater than 90%. These models are suitable for real-time use due to their small computational complexity. Even though they have high accuracy, these models are static and cannot adapt to environmental change. To maintain the accuracy of the prediction models, the authors have adopted Online Learning techniques that update the models on data from subjective viewer feedback. This method provides accurate and adaptive QoE prediction models that are an indispensible component of a QoE-aware management service.

scholarly journals A quality of experience-aware cross-layer architecture for optimizing video streaming services

2020 ◽  
Author(s):  
qahhar muhammad qadir ◽  
Alexander A. Kist ◽  
ZHONGWEI ZHANG
Keyword(s):  
Video Streaming ◽  
Quality Of Experience ◽  
Video Quality ◽  
The Internet ◽  
Cross Layer ◽  
Video Traffic ◽  
Adaptive Architecture ◽  
Streaming Services ◽  
Simulation Results

The popularity of the video services on the Internet has evolved various mechanisms that target the Quality of Experience (QoE) optimization of video traffic. The video quality has been enhanced through adapting the sending bitrates. However, rate adaptation alone is not sufficient for maintaining a good video QoE when congestion occurs. This paper presents a cross-layer architecture for video streaming that is QoE-aware. It combines adaptation capabilities of video applications and QoE-aware admission control to optimize the trade-off relationship between QoE and the number of admitted sessions. Simulation results showed the efficiency of the proposed architecture in terms of QoE and number of sessions compared to two other architectures (adaptive architecture and non-adaptive architecture ).

scholarly journals Diseño de un modelo de atención visual para campañas de comunicación. El caso de la Covid-19

2020 ◽  
Author(s):  
Juan-José Silva-Torres ◽  
Luz Martínez-Martínez ◽  
Ubaldo Cuesta-Cambra
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Eye Tracking ◽  
Influenza A ◽  
Social Consequences ◽  
Machine Learning Techniques ◽  
Mathematical Tool ◽  
Learning Techniques ◽  
Mathematical Techniques

Health is one of the main concerns of society. Empirical evidence underscores the growing importance of prevention and health education as a fundamental instrument to improve the quality of public health. Recent health crises, such as Ebola, influenza A, SARS, and Covid-19, have highlighted the importance of communication. When designing communication campaigns during a crisis, the speed of the creation of messages and their effectiveness have relevant social consequences. The objective of this work is to design and develop a mathematical tool, based on Machine Learning techniques, to enable predictions of areas of visual attention quickly and accurately without the use of eye-tracking technology. The methodology combines deep learning algorithms, to extract the characteristics of the images, and supervised modeling mathematical techniques, to predict the areas of attention. Validation is carried out by analyzing various institutional communications from the Covid-19 campaign, comparing the results with the areas of attention obtained using an eye-tracking solution with proven accuracy. The results obtained using the tool in the investigated Covid-19 communication pieces are analyzed, resulting in conclusions of interest for the development of new campaigns. Resumen La salud es una de las principales preocupaciones de la sociedad. La evidencia empírica subraya la importancia creciente de la prevención y la educación para la salud (EpS) como instrumento fundamental para mejorar la calidad de la salud pública. Las recientes crisis sanitarias, como la del Ébola, la gripe A, el SARS o la Covid-19, han puesto de relieve la importancia de la comunicación. En el diseño de campañas de comunicación en tiempos de crisis, la rapidez en la creación de mensajes y la validación de su eficacia tiene consecuencias sociales relevantes. En este trabajo se ha diseñado y desarrollado una herramienta matemática basada en machine learning que permite predecir las áreas de atención visual de forma rápida y precisa, sin necesidad de utilizar tecnología de seguimiento ocular (eye-tracking). La metodología combina algoritmos de deep learning, para extraer las características de las imágenes, con técnicas de modelización supervisada para predecir las áreas de atención. La validación se ha realizado analizando varias comunicaciones institucionales de la campaña de Covid-19, comparando los resultados con las áreas de atención obtenidas con una solución de seguimiento ocular de validez contrastada. Se analizan los resultados obtenidos mediante la herramienta en las piezas de comunicación Covid-19 investigadas, planteando conclusiones de interés para el desarrollo de nuevas campañas.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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